{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: replacing module ALBreIF.\n",
      "WARNING: replacing module BPGF.\n"
     ]
    }
   ],
   "source": [
    "include(\"ALBreIF/ALBreIF.jl\")\n",
    "include(\"BPGF/BPGF.jl\")\n",
    "using .ALBreIF\n",
    "using .BPGF"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0 0.0 … 0.0 0.0; 0.0 0.0004977541027020962 … 0.0 0.0; … ; 0.013519237914985964 0.008375842436292977 … 0.0 0.0; 0.007977066207710486 0.008578201861637224 … 0.0 0.0], [0.02391208118294609 0.02338630515624623 … 0.03061903237732982 0.005780108471948842; 0.0022699264657863876 0.01727483144939871 … 0.01907494664946117 0.01667492458883313; … ; 0.015432751130652761 0.0213284193508436 … 0.004521228058738259 0.02333706663611868; 0.010669499694007718 0.00902910324832745 … 0.005500596512997219 0.019716375453506796])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Images\n",
    "ul = \"ORL_Faces/s1/1.pgm\"\n",
    "ig = load(ul)\n",
    "\n",
    "a, b = size(ig)\n",
    "R = 5\n",
    "\n",
    "B = Matrix{Float64}(undef, a*b, 400)\n",
    "for i = 1 : 40\n",
    "    for j = 1 : 10\n",
    "        local url = \"ORL_Faces/s\"*\"$i/\"*\"$j\"*\".pgm\"\n",
    "        local img = load(url)\n",
    "        local q = float64.(channelview(img))\n",
    "        q = q[:]\n",
    "        global B[:, (i-1)*10 + j] = q\n",
    "    end\n",
    "end\n",
    "\n",
    "A = BPGF.normalize!(B)\n",
    "X, Y = BPGF.randinit(A, R^2, 0.2, 1.0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ρ₀ = 0.2 # step size parameter\n",
    "μ₀ = 0.0001 # regularization cofficient\n",
    "μ = 0.0 # another regularization coefficient\n",
    "rtime = 300 # runtime\n",
    "version = false"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main.ALBreIF.Result{Float64}([0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.004613006642848767 0.0009260491873237566 … 0.0 0.0; 0.00570420907206282 0.007123074433680732 … 0.0 0.0], [0.014454534391943046 0.014110883270459286 … 0.013628903646114087 0.01235932942047727; 0.01393580121912667 0.014609145494637859 … 0.013568426057089888 0.012177229275234574; … ; 0.01452414389927445 0.016073383691484287 … 0.015436682649540222 0.012777837738506232; 0.01345783677587599 0.016175587498164137 … 0.01371984495898817 0.015632642063720545], 1590, false, [0.0 1.0000031513086112; 0.22699999809265137 0.9745340644317437; … ; 0.0 0.0; 0.0 0.0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X₀ = copy(X); # OutOfMemoryError\n",
    "# Y₀ = copy(Y);\n",
    "# r₀ = BPGF.solve!(BPGF.BPG{Float64}(runtime=rtime,\n",
    "#                 verbose=version,\n",
    "#                 ρ=0.02,\n",
    "#                 μ₁=μ₀,\n",
    "#                 μ₂=μ), A, X₀, Y₀)\n",
    "\n",
    "\n",
    "X₁ = copy(X);\n",
    "Y₁ = copy(Y);\n",
    "r₁ = BPGF.solve!(BPGF.BBPG{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.2,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₁, Y₁)\n",
    "\n",
    "X₂ = copy(X);\n",
    "Y₂ = copy(Y);\n",
    "r₂ = ALBreIF.solve!(ALBreIF.ABLBreI{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.5,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₂, Y₂)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store experimental results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1590-element Vector{Float64}:\n",
       " 1.0000031513086112\n",
       " 0.9745340644317437\n",
       " 0.9486629900085723\n",
       " 0.9230210333311009\n",
       " 0.9008147253827506\n",
       " 0.8774040888643879\n",
       " 0.8545421694331832\n",
       " 0.8333972015568\n",
       " 0.8132861562151241\n",
       " 0.7932506782198625\n",
       " ⋮\n",
       " 0.08729620889312209\n",
       " 0.0872833754385033\n",
       " 0.08728210372542694\n",
       " 0.0872806165627012\n",
       " 0.08727846796342272\n",
       " 0.08727662298967769\n",
       " 0.08727450076202352\n",
       " 0.08727273120007094\n",
       " 0.08725955543418115"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stop₀ = r₀.niters\n",
    "# pic₀ = r₀.objvalue\n",
    "# rt₀ = pic₀[1:stop₀, 1]\n",
    "# obj₀ = pic₀[1:stop₀, 2]\n",
    "\n",
    "stop₁ = r₁.niters\n",
    "pic₁ = r₁.objvalue\n",
    "rt₁ = pic₁[1:stop₁, 1]\n",
    "obj₁ = pic₁[1:stop₁, 2]\n",
    "\n",
    "stop₂ = r₂.niters\n",
    "pic₂ = r₂.objvalue\n",
    "rt₂ = pic₂[1:stop₂, 1]\n",
    "obj₂ = pic₂[1:stop₂, 2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using CairoMakie\n",
    "using LaTeXStrings\n",
    "using Colors\n",
    "using AlgebraOfGraphics\n",
    "CairoMakie.activate!()\n",
    "function speed()\n",
    "        # lines(rt₀, obj₀; color=\"#4063D8\", label=\"BPG\", linewidth=3, linestyle=:dashdot,\n",
    "                # )\n",
    "        lines(rt₁, obj₁; color=\"blue\", linewidth=2, linestyle=:solid,\n",
    "                label=\"BBPG\",\n",
    "                figure=(; figure_padding=50, resolution=(1200, 800), font=\"sans\",\n",
    "                        backgroundcolor=:white, fontsize=32),\n",
    "                axis=(; title = L\"\\text{ORL Datasets-Regularization}(\\mu=10^{-4})\",\n",
    "                        xlabel=\"Time(sec)\", ylabel=L\"\\Vert A-XY\\Vert_F\",\n",
    "                        yscale=log10,\n",
    "                        xgridstyle=:dash, ygridstyle=:dash,\n",
    "                        topspinevisible = false, rightspinevisible =false))\n",
    "        lines!(rt₂, obj₂; color=\"#CB3C33\", linewidth=2, linestyle=:solid,\n",
    "                label=\"ABLBreI\")\n",
    "        limits!(0, rtime, 10^(-1.2), 1.0)\n",
    "        axislegend(merge=true)\n",
    "        current_figure()\n",
    "end\n",
    "\n",
    "with_theme(speed, theme=theme_dark())\n",
    "speed()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CairoMakie.Screen{IMAGE}\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save(\"plot/real_data_1.png\", speed())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "find_closest (generic function with 1 method)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function find_closest(value, arr)\n",
    "    min_diff = abs(arr[1] - value)\n",
    "    closest_index = 1\n",
    "    \n",
    "    for i in 2:length(arr)\n",
    "        diff = abs(arr[i] - value)\n",
    "        if diff < min_diff\n",
    "            min_diff = diff\n",
    "            closest_index = i\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return closest_index\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19216623256921286\n",
      "0.13259308428874472\n",
      "0.1015954494546189\n",
      "0.08725955543418115"
     ]
    }
   ],
   "source": [
    "print(obj₂[find_closest(15, rt₂)])\n",
    "print(\"\\n\")\n",
    "print(obj₂[find_closest(30, rt₂)])\n",
    "print(\"\\n\")\n",
    "print(obj₂[find_closest(60, rt₂)])\n",
    "print(\"\\n\")\n",
    "print(obj₂[find_closest(300, rt₂)])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.8.3",
   "language": "julia",
   "name": "julia-1.8"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
